Retinal capillary density among healthy Egyptian and South Asian students:an optical coherence tomography angiography study

AIM:To compare the macular and optic nerve perfusion and vascular architecture using optical coherence tomography angiography(OCTA)in normal eyes of Egyptian(Caucasians)and South Asian(Asians)volunteers.METHODS:Cross-sectional analytical OCTA study performed on 90 eyes of South Asian(n=45)and Egyptians(n=45)were analyzed.All participants underwent bestcorrected visual acuity test,slit lamp,and fundus examination.OCTA images;macular 6×6 mm^(2) grid and optic nerve 4.5×4.5 mm^(2) grid were used to examine the parafoveal and peripapillary regions,respectively.RESULTS:The mean capillary vessel density(CVD)in macular sectors among South Asians and Egyptians participants were(50.31%±2.53%,51.2%±5.93%)and(49.71%±3.6%,51.94%±4.79%)in superficial(SCP)and deep capillary plexuses(DCP),respectively(P>0.05).Mean CVD in both groups was higher in DCP compared to SCP in all sectors but was not significant(P>0.05).Mean foveal CVD increases with an increase in central retinal thickness in both SCP and DCP(P<0.001),among both groups.Mean area of the foveal avascular zone(FAZ)was 0.28±0.09 and 0.27±0.08 mm2 in South Asian and Egyptians,respectively.FAZ area decreases with an increase in the thickness and foveal CVD(P<0.001).Mean CVD in the peripapillary area was 48.23%±5.78% in South Asian and 49.52%±2.38% in Egyptian volunteers.The mean retinal nerve fiber layer thickness was found to be higher in the nasal quadrant among South Asian females than the Egyptian females(P<0.05).CONCLUSION:No significant racial disparity is found in this study.The findings are helpful for assessing and improving the normative data on the differences in South Asian and Egyptian populations.

Bioinspired Polarized Optical Flow Enables Turbid Underwater Target Motion Estimation

Underwater target motion estimation is a challenge for ocean military and scientific research.In this work,we propose a method based on the combination of polarization imaging and optical flow for turbid underwater target detection.Polarization imaging can reduce the influence of backscattered light and obtain high-quality images underwater.The optical flow shows the motion and structural information of the target.We use polarized optical flow to obtain the optical flow field and estimate the target motion.The experimental results of different targets under varying water turbidity levels illustrate that our method is realizable and robust.The precision is verified by comparing the results with the precise displacement data and calculating two error measures.The proposed method based on polarized optical flow can obtain accurate displacement information and a good recognition effect.Moving target segmentation based on the Otsu method further proves the superiority of the polarized optical flow under turbid water.This study is valuable for target detection and motion estimation in scattering environments.

Phase-Based Optical Flow Method with Optimized Parameter Settings for Enhancing Displacement Measurement Adaptability

To enhance the applicability and measurement accuracy of phase-based optical flow method using complex steerable pyramids in structural displacement measurement engineering applications, an improved method of optimizing parameter settings is proposed. The optimized parameters include the best measurement points of the Region of Interest (ROI) and the levels of pyramid filters. Additionally, to address the issue of updating reference frames in practical applications due to the difficulty in estimating the maximum effective measurement value, a mechanism for dynamically updating reference frames is introduced. Experimental results demonstrate that compared to representative image gradient-based displacement measurement methods, the proposed method exhibits higher measurement accuracy in engineering applications. This provides reliable data support for structural damage identification research based on vibration signals and is expected to broaden the engineering application prospects for structural health monitoring.

Discontinuity-preserving optical flow algorithm

A modification of Horn and Schunk＇s approach is investigated, which leads to a better preservation of flow discontinuities. It improves Horn-Schunk model in three aspects： （1） It replaces the smooth weight coefficient in the energy equation by the variable weight coefficient. （2） It adopts a novel method to compute the mean velocity. The novel method also reflects the effect of the intensity difference on the image velocity diffusion. （3） It introduces a more efficient iterative method than the Gauss-Seidel method to solve the associated Euler-Lagrange equation. The experiment results validate the better effect of the improved method on preserving discontinuities.

A novel algorithm for ocean wave direction inversion from X-band radar images based on optical flow method

As one of the important sea state parameters for navigation safety and coastal resource management, the ocean wave direction represents the propagation direction of the wave. A novel algorithm based on an optical flow method is developed for the ocean wave direction inversion of the ocean wave fields imaged by the X-band radar continuously. The proposed algorithm utilizes the echo images received by the X-band wave monitoring radar to estimate the optical flow motion, and then the actual wave propagation direction can be obtained by taking a weighted average of the motion vector for each pixel. Compared with the traditional ocean wave direction inversion method based on frequency-domain, the novel algorithm is fully using a time-domain signal processing method without determination of a current velocity and a modulation transfer function（MTF）. In the meantime,the novel algorithm is simple, efficient and there is no need to do something more complicated here. Compared with traditional ocean wave direction inversion method, the ocean wave direction of derived by using this proposed method matches well with that measured by an in situ buoy nearby and the simulation data. These promising results demonstrate the efficiency and accuracy of the algorithm proposed in the paper.

Contributions to Hom-Schunck optical flow equations-part I： Stability and rate of convergence of classical algorithm

Globally exponential stability （which implies convergence and uniqueness） of their classical iterative algorithm is established using methods of heat equations and energy integral after embedding the discrete iteration into a continuous flow. The stability condition depends explicitly on smoothness of the image sequence, size of image domain, value of the regularization parameter, and finally discretization step. Specifically, as the discretization step approaches to zero, stability holds unconditionally. The analysis also clarifies relations among the iterative algorithm, the original variation formulation and the PDE system. The proper regularity of solution and natural images is briefly surveyed and discussed. Experimental results validate the theoretical claims both on convergence and exponential stability.

Research on HS Optical Flow Algorithm Based on Motion Estimation Optimization

The amount of computation for detecting moving objects by the optical flow algorithm is large. The optical flow information in the smooth region cannot be detected by the optical flow algorithm, and it is susceptible to noise in a complicated environment. In this study, an optimized Horn-Schunck (HS) optical flow algorithm based on motion estimation is proposed. To detect Harris corner in the image, the proposed algorithm is used in combination with the motion estimation algorithm based on macroblock to determine the region of interest (ROI) [1]. The ROI is then used as the initial motion vector for HS calculation to obtain the optical flow information. Filtering is conducted to eliminate the background noise. Experimental result shows that the application of the proposed algorithm improves the computational speed, avoids the interference of background noise, and enhances the robustness of HS. Moreover, the algorithm solves the problem rooted in the inability of the HS algorithm to detect the smooth part of optical flow information [2].

Obstacle Avoidance of Flapping⁃Wing Air Vehicles Based on Optical Flow and Fuzzy Control

The flapping-wing air vehicle(FWAV)is a kind of bio-inspired robot whose wings can flap up and down like bird and insect wings.A vision-based obstacle avoidance method for FWAVs is proposed in this paper.First,the Farneback algorithm is used to calculate the optical flow field of the first-view video frames taken by the on-board image transmission camera.Based on the optical flow information,a fuzzy obstacle avoidance controller is then designed to generate the FWAV steering commands.Experimental results show that the proposed obstacle avoidance method can accurately identify obstacles and achieve obstacle avoidance for FWAVs.

6-DOF motion estimation using optical flow based on dual cameras

Because of its characteristics of simple algorithm and hardware, optical flow-based motion estimation has become a hot research field, especially in GPS-denied environment. Optical flow could be used to obtain the aircraft motion information, but the six-(degree of freedom)(6-DOF) motion still couldn't be accurately estimated by existing methods. The purpose of this work is to provide a motion estimation method based on optical flow from forward and down looking cameras, which doesn't rely on the assumption of level flight. First, the distribution and decoupling method of optical flow from forward camera are utilized to get attitude. Then, the resulted angular velocities are utilized to obtain the translational optical flow of the down camera, which can eliminate the influence of rotational motion on velocity estimation. Besides, the translational motion estimation equation is simplified by establishing the relation between the depths of feature points and the aircraft altitude. Finally, simulation results show that the method presented is accurate and robust.

Retrieval of inherent optical properties of the Yellow Sea and East China Sea using a quasi-analytical algorithm

We tested and modified the quasi-analytical algorithm （QAA） using 57 groups of field data collected in the spring of 2003 in the Yellow Sea and East China Sea. The QAA performs well in deriving total absorption coefficients of typical coastal waters. The average percentage difference （APD） is in a range of 13.9%-38.5% for the total absorption coefficient （13.9% at 440 nm）, and differences in particle backscattering coefficient bbp（2） are less than 50% （in the case of the updated QAA）. To obtain improved results, we modified the QAA by adjusting the empirical relationships. The modified algorithm is then applied to the field data to test its performance. The APDs were 44.7%-46.6% for bbp（λ） and 9.9%-32.8% （9.9% at 555 nm） for the total absorption coefficient. This indicates that the modified QAA derives better results. We also used the modified model to derive phytoplankton pigment absorption （aph） and detritus and CDOM absorption （aug） coefficients. The APDs for aph and a dg at 440 nm are 37.1% and 19.8%. In this paper, we discuss error sources using the measured dataset. More independent field data can improve this algorithm and derive better results.

Algorithmic approach to discrete fracture network flow modeling in consideration of realistic connections in large-scale fracture networks

Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.

Modelling the temporal-varied nonlinear velocity profile of debris flow using a stratification aggregation algorithm in 3D-HBP-SPH framework

Estimation of velocity profile within mud depth is a long-standing and essential problem in debris flow dynamics.Until now,various velocity profiles have been proposed based on the fitting analysis of experimental measurements,but these are often limited by the observation conditions,such as the number of configured sensors.Therefore,the resulting linear velocity profiles usually exhibit limitations in reproducing the temporal-varied and nonlinear behavior during the debris flow process.In this study,we present a novel approach to explore the debris flow velocity profile in detail upon our previous 3D-HBPSPH numerical model,i.e.,the three-dimensional Smoothed Particle Hydrodynamic model incorporating the Herschel-Bulkley-Papanastasiou rheology.Specifically,we propose a stratification aggregation algorithm for interpreting the details of SPH particles,which enables the recording of temporal velocities of debris flow at different mud depths.To analyze the velocity profile,we introduce a logarithmic-based nonlinear model with two key parameters,that a controlling the shape of velocity profile and b concerning its temporal evolution.We verify the proposed velocity profile and explore its sensitivity using 34 sets of velocity data from three individual flume experiments in previous literature.Our results demonstrate that the proposed temporalvaried nonlinear velocity profile outperforms the previous linear profiles.

Planning of a Single Flow Channel in Valve Blocks Based on Additive Manufacturing and the Ant Colony Algorithm

As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks intensifies.However,owing to the constraints imposed by traditional manufacturing processes,conventional hydraulic integrated valve blocks fail to satisfy the demands of a more compact channel layout and lower energy dissipation.Notably,the subjectivity in the arrangement of internal passages results in a time-consuming and labor-intensive process.This study employed additive manufacturing technology and the ant colony algorithm and B-spline curves for the meticulous design of internal passages within an aviation EHA valve block.The layout environment for the valve block passages was established,and path optimization was achieved using the ant colony algorithm,complemented by smoothing using B-spline curves.Three-dimensional modeling was performed using SolidWorks software,revealing a 10.03%reduction in volume for the optimized passages compared with the original passages.Computational fluid dynamics(CFD)simulations were performed using Fluent software,demonstrating that the algorithmically optimized passages effectively prevented the occurrence of vortices at right-angled locations,exhibited superior flow characteristics,and concurrently reduced pressure losses by 34.09%-36.36%.The small discrepancy between the experimental and simulation results validated the efficacy of the ant colony algorithm and B-spline curves in optimizing the passage design,offering a viable solution for channel design in additive manufacturing.

An Underwater Image Bubble Noise Removal Method Based on Optical Flow

It is very important for underwater robots to accurately detect and locate target objects. However,under many circumstances,it is difficult to clearly observe the target object due to the existence of bubble noise. In this paper,we proposed a method to solve this problem. First,we used the LK optical flow algorithm to calculate the motion vector of the image background and compensate for the background motion.Then,the optical flow field of the bubbles was calculated by the HS optical flow algorithm,and the area where the bubble existed was obtained by binarizing the image. Finally,we used the adjacent frame image to repair the bubble area. We carried out a bubble noise removal experiment. The results show that this method can effectively remove the bubble noise in the image.

Analysis of spectrum characteristics of optical scintillation in stack gas flow

Based on the analysis of spectrum characteristics of intensity fluctuations while light beams pass through stack gas flow in an industrial setting, this paper puts emphasis upon discussing the spectrum of optical intensity fluctuations by the variety of particle concentration in stack gas flow. This paper also gives the primary theoretical explanation of the measurement results in the stack of coal-fired utility boilers. Meanwhile, the cross-correlation formula is given as the theoretical basis of velocity measurement by using particle concentration scintillation.

Simulation of unmanned survey path planning in debris flow gully based on GRE-Bat algorithm

Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and most difficult problems faced by unmanned surveys of debris flow valleys.This study proposes a new hybrid bat optimization algorithm,GRE-Bat(Good point set,Reverse learning,Elite Pool-Bat algorithm),for unmanned exploration path planning of debris flow sources in outdoor environments.In the GRE-Bat algorithm,the good point set strategy is adopted to evenly distribute the population,ensure sufficient coverage of the search space,and improve the stability of the convergence accuracy of the algorithm.Subsequently,a reverse learning strategy is introduced to increase the diversity of the population and improve the local stagnation problem of the algorithm.In addition,an Elite pool strategy is added to balance the replacement and learning behaviors of particles within the population based on elimination and local perturbation factors.To demonstrate the effectiveness of the GRE-Bat algorithm,we conducted multiple simulation experiments using benchmark test functions and digital terrain models.Compared to commonly used path planning algorithms such as the Bat Algorithm(BA)and the Improved Sparrow Search Algorithm(ISSA),the GRE-Bat algorithm can converge to the optimal value in different types of test functions and obtains a near-optimal solution after an average of 60 iterations.The GRE-Bat algorithm can obtain higher quality flight routes in the designated environment of unmanned investigation in the debris flow gully basin,demonstrating its potential for practical application.

A Closed Form Solution to Segment 3D Motion Using Straight-line Optical Flow

A closed form solution to the problem of segmenting multiple 3D motion models was proposed from straight-line optical flow. It introduced the multibody line optical flow constraint (MLOFC), a polynomial equation relating motion models and line parameters. The motion models can be obtained analytically as the derivative of the MLOFC at the corresponding line measurement, without knowing the motion model associated with that line. Experiments on real and synthetic sequences were also presented.

Study on Local Optical Flow Method Based on YOLOv3 in Human Behavior Recognition

In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only Look Once v3) and local optical flow method. Based on the dense optical flow method, the optical flow modulus of the area where the human target is detected is calculated to reduce the amount of computation and save the cost in terms of time. And then, a threshold value is set to complete the human behavior identification. Through design algorithm, experimental verification and other steps, the walking, running and falling state of human body in real life indoor sports video was identified. Experimental results show that this algorithm is more advantageous for jogging behavior recognition.

APPLICATION OF DOPPLER OPTICAL COHERENCE TOMOGRAPHY IN RHEOLOGICAL STUDIES:BLOOD FLOW AND VESSELS MECHANICAL PROPERTIES EVALUATION

Doppler Optical Coherence Tomography(DOCT)is a noninvasive optical diagnostic technique,which is well suited for the quantitative mapping of microflow velocity profiles and the analysis of flow-vessel interactions.The noninvasive imaging and quantitative analysis of blood flow in the complex-structured vascular bed is required in many biomedical applications,including those where the determination of mechanical properties of vessels or the knowledge of the mechanic interactions between the flow and the housing medium plays a key role.The change of microvessel wall elasticity could be a potential indicator of cardiovascular disease at the very early stage,whilst monitoring the blood flow dynamics and associated temporal and spatial variations in vessel’s wall shear stress could help predicting the possible rupture of atherosclerotic plaques.The results of feasibility studies of application of DOCT for the evaluation of mechanical properties of elastic vessel model are presented.The technique has also been applied for imaging of sub-cranial rat blood flow in vivo.